scholarly journals Shape-invariant encoding of dynamic primate facial expressions in human perception

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Nick Taubert ◽  
Michael Stettler ◽  
Ramona Siebert ◽  
Silvia Spadacenta ◽  
Louisa Sting ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Visual Processing ◽  
Human Perception ◽  
Network Models ◽  
Neuromuscular Control ◽  
Expression Recognition ◽  
Neural Network Models ◽  
Facial Shape ◽  
Alternative Hypotheses

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.

scholarly journals Shape-invariant perceptual encoding of dynamic facial expressions across species

2020 ◽  
Author(s):  
N. Taubert ◽  
M. Stettler ◽  
R. Siebert ◽  
S. Spadacenta ◽  
L. Sting ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Visual Processing ◽  
Neuromuscular Control ◽  
Expression Recognition ◽  
Facial Shape ◽  
Dynamic Expression ◽  
Dynamic Facial Expressions ◽  
Species Specific ◽  
Joint Encoding

AbstractDynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural-network theories predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate this hypothesis, we developed photo-realistic human and monkey heads that were animated with motion-capture data from monkeys and human. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented independently of facial shape. This result supports the co-evolution of the visual processing and motor-control of facial expressions, while it challenges popular neural-network theories of dynamic expression-recognition.

scholarly journals Understanding fully-connected and convolution allayers in unsupervised learning using face images

2019 ◽  
Author(s):  
Lucas Fontes Buzuti ◽  
Carlos Eduardo Thomaz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Facial Expressions ◽  
Multivariate Statistics ◽  
Network Models ◽  
Neural Network Models ◽  
Face Images ◽  
Convolutional Autoencoder ◽  
Fully Connected

The goal of this paper is to implement and compare two unsupervised models of deep learning: Autoencoder and Convolutional Autoencoder. These neural network models have been trained to learn regularities in well-framed face images with different facial expressions. The Autoencoder's basic topology is addressed here, composed of encoding and decoding multilayers. This paper approaches these automatic codings using multivariate statistics to visually understand the bottleneck differences between the fully-connected and convolutional layers and the corresponding importance of the dropout strategy when applied in a model.

Material Demands for Optical Neural Networks

MRS Bulletin ◽  
1988 ◽  
Vol 13 (8) ◽  
pp. 30-35 ◽  
Author(s):  
Dana Z. Anderson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Processing ◽  
Visual Processing ◽  
Network Models ◽  
Human Memory ◽  
Information Storage ◽  
Physical Damage ◽  
Neural Network Models ◽  
The Brain

From the time of their conception, holography and holograms have evolved as a metaphor for human memory. Holograms can be made so that the information they contain is distributed throughout the holographic medium—destroy part of the hologram and the stored information remains wholly intact, except for a loss of detail. In this property holograms evidently have something in common with human memory, which is to some extent resilient against physical damage to the brain. There is much more to the metaphor than simply that information is stored in a distributed manner.Research in the optics community is now looking to holography, in particular dynamic holography, not only for information storage, but for information processing as well. The ideas are based upon neural network models. Neural networks are models for processing that are inspired by the apparent architecture of the brain. This is a processing paradigm that is new to optics. From within this network paradigm we look to build machines that can store and recall information associatively, play back a chain of recorded events, undergo learning and possibly forgetting, make decisions, adapt to a particular environment, and self-organize to evolve some desirable behavior. We hope that neural network models will give rise to optical machines for memory, speech processing, visual processing, language acquisition, motor control, and so on.

scholarly journals To find better neural network models of human vision, find better neural network models of primate vision

2019 ◽  
Author(s):  
Kamila Maria Jozwik ◽  
Martin Schrimpf ◽  
Nancy Kanwisher ◽  
James J. DiCarlo
Keyword(s):  
Neural Network ◽  
Visual Processing ◽  
Network Models ◽  
Human Vision ◽  
Current Standard ◽  
Neural Network Models ◽  
Model Match ◽  
Primate Vision ◽  
Brain Processing ◽  
Human Primate

AbstractSpecific deep artificial neural networks (ANNs) are the current best models of ventral visual processing and object recognition behavior in monkeys. We here explore whether models of non-human primate vision generalize to visual processing in the human primate brain. Specifically, we asked if model match to monkey IT is a predictor of model match to human IT, even when scoring those matches on different images. We found that the model match to monkey IT is a positive predictor of the model match to human IT (R = 0.36), and that this approach outperforms the current standard predictor of model accuracy on ImageNet. This suggests a more powerful approach for pre-selecting models as hypotheses of human brain processing.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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